1. Field of the Invention
The present invention relates to the technical field of non-destructive testing, and the present invention particularly relates to a method and a device for the classification of an object to be tested into a quality class using acoustic signals.
2. Description of the Related Art
In order to be able to determine the wear or the remaining lifetime of an object, such as railway wheels, there are often used methods that do not destroy the object to be tested, so that this object may still be used according to its use. To allow such non-destructive testing, there are particularly used acoustic signals for such wear or remaining lifetime testing, because they have particularly good propagation properties in solids. An acoustic excitation signal is then applied to an object to be tested and a received impulse response to the excitation signal is stored digitally via sensors. From this received signal, i.e. the impulse response of the excitation signal, there is then performed a calculation of a short-time FFT in several time windows, which allows observation of the signal in the frequency domain. The result is represented in the form of a spectral representation (spectrogram). Therein, signal energy of the received impulse response signal is plotted as a function of time and frequency. This allows comprehensive characterization of the relevant oscillation modes and its attenuation behavior.
A precondition for an evaluation of the devices or objects to be tested is in most cases the performing of a training process in a preceding step. For this purpose, there is used a representative selection of good parts for the formation of a reference pattern.
The distance to the reference pattern is determined. Based thereon, a decision is made whether the device is good or faulty.
The introduction of a multi-class model allowed to manually define the classes created by this alteration as further good classes. When deviations from the training class occurred, the part or object was no longer defined as bad, instead there was the formation of a new class that was initially considered as undefined class and only by manual testing obtained the rating “good” and/or “bad”.
For such a classification of the devices or objects, it has already been possible to successfully use the approach to classify structured, non-voice signals with methods of voice processing. In some cases, a simple DTW detector (DTW=dynamic time warping) may be used for this purpose, such as suggested, for example, in P. Holstein, M. Koch, D. Hirschfeld, R. Hoffmann, D. Bader, K. Augsburg: “A Strategy for Signal Recognition under Adverse Conditions” in: Proc. 32nd Conf. Internoise, 2003, Jeju Korea. In complicated cases, the concept of the hidden Markov models (HMM) is used, as it is exemplarily illustrated in FIG. 5. The application of such an HMM concept was, for example, suggested in the following documents: D. Zhang Y. Zeng, X. Zhou, Cheng Y: “The pattern recognition of non-destructive testing based on HMM”, in: Proc. 4th World Congress on Intelligent Control and Automation (Cat. No. 02EX527), 2002, vol. 3, pp. 2198-2202, Piscataway, N. J., USA; P. Baruah and R. B. Chinnma: “HMMs for diagnostics and prognostics in machining processes,” in: Proc. 57th Meeting of the Society for Machinery Failure Prevention Technology, 2003, pp. 389-398, Virginia Beach, USA; A. R. Taylor and S. R. Duncan: “A comparison of techniques for monitoring process faults,” in: Proc. Conf. Control Systems, 2002, pp. 323-327, Stockholm, SE; H. Y. K. Lau: “A hidden markov model-based assembly contact recognition system,” Mechatronics, vol. 13(8-9), pp. 1001-1023, 2003, ISSN 0957-4158.
Due to the well-defined sequential structure of a voice signal, hidden Markov models usually use simple left-right graphs, such as graph 500 in FIG. 5. These graphs consist of a set of so-called hidden nodes 1, . . . , 5 (also called states) connected to each other, and a function associating feature vectors with the states. Node 1 represents the input node of the hidden Markov model, and node 5 represents the output node of the hidden Markov model. The individual nodes 1 to 5 are connected to each other by connections 502 reflecting a transition probability from one node to a following node. The function associating feature vectors with the states is, in most cases, a mixture of Gauss distribution density functions in the feature space for each inner state of the HMM.
The limits of the conventional methods described above are, in most cases, that such methods react to slight production changes not affecting the quality of the device by sorting out good parts. Although the use of multi-class models is a first step in the solution of this problem, a relative large number of classes are generated in this way, which each time require the decision of the user whether the new class is a good or a bad class. The use of HMMs already achieves good results in simply structured signals. However, since signals occurring with more complicated problems of the non-destructive testing generally do not have simple structures like the left-right structure described above, such simple left-right structures cannot offer any determination of a quality class of the device to be tested or only a very imprecise determination.
Furthermore, the conference contribution, F. Wolfertstetter and G. Ruske: “Structured Markov models for speech recognition”, in: Proc. ICASSP, 1995, pp. 544-547, Detroit, USA suggests an approach to use extended structure models for voice processing. These extended structure models in the form of stochastic Markov graphs allow improved synthesis and recognition of voice. However, this approach has proven to be disadvantageous because voice signals, as compared to the acoustic test signals mentioned above, are generated by completely different processes and thus also have different properties. A simple adoption of the special structure models in form of stochastic Markov graphs for non-destructive testing may thus not be done easily.
Also, in a conference contribution, M. Fichner, M. Wolff and R. Hoffmann: “A unified approach for speech synthesis and speech recognition using stochastic Markov graphs,” in Proc. 6th Int. Conf. Spoken Language Processing (ICSLP), 2000, vol. 1, pp. 701-704, Beijing, PR China, and in the conference contribution, M. Eichner, S. Ohnewald, M. Wolff and R. Hoffmann: “Speech synthesis using Stochastic Markov Graphs,” in: Proc. ICASSP, May 5-7, 2001, Salt Lake City, Utah, USA, there is suggested a possibility for voice recognition and/or voice synthesis on the basis of complex structure models in the form of stochastic Markov graphs. The above disadvantages and/or problems with the transfer of the complex structure models from voice processing to a non-destructive testing method apply here, too.